Generalization can be divided into two types, namely model
generalization and cartographic generalization. These two types are closely
related, often model generalization being a pre-process of cartographic
generalization. This paper aims to develop the model generalization and emphasize
the progressive idea. The bases of this research are the theory of constraint-based
generalization and some generalization models which use graph and tree
structure to solve the generalization of urban street network. This paper
presents a new generalization model for selecting important and characteristic
streets in an urban street network. This model is based on constraints and a
novel data structure. The constraints can be used as specification, guidelines
and evaluation standard. The data structure is a supporting tool and essential
for the automation of generalization. We show that this model can generalize
the street network step by step in the horizontal dimension and represent
multi-scale results in the vertical dimension. The model has some advantages
that it produces predictable result, minimizes the deviation and maintains the
integrity of the objects.

At the beginning, constraints which are used in generalization of
urban street network are described as controlling factors to generalization,
mainly including functional, graphical and structural constraints. Functional
constraints identify the preserved streets during the process; Graphical
constraints specify the minimum length of streets and the minimum area of
blocks surrounded by streets; Structural constraints maintain the quantity of
streets, the streets density and the size relationship among blocks and streets
according to the map scale. Then characterization of the constraints is
illustrated and some parameters are defined, including street hierarchy, street
function, street connectivity, block area, street length, street quantity and
street density. Further, a data structure is introduced in an attempt to partly
circumvent the problem of urban street network generalization. It relies on a
topological structure based on faces and edges. The face represents the block
and the edge represents medial axis of the street. Based on this
representation, the constraints are applied in the data structure to guide how
to merge the blocks and maintain the structure of the street network. The
process is driven by calculating the importance of blocks and streets
surrounding them. This data structure turns out to be a set of trees. The
presented data structure is suitable for progressive automated generalization.
The proposed approach is validated by a case study.

Innovations of this research are: in theoretic level, it emphasizes
the important role of the progressive idea in generalization. In methodological
level, it introduces constraints to a data structure to solve the specified
problem while considering the factors comprehensively.